Prediction and validation of anoikis-related genes in neuropathic pain using machine learning.

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0314773
Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen
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引用次数: 0

Abstract

Background: Neuropathic pain (NP) can be induced by a variety of clinical conditions, such as spinal cord injury, lumbar disc herniation (LDH), lumbar spinal stenosis, diabetes, herpes zoster, and spinal cord tumors, and inflammatory stimuli. The pathogenesis of NP is extremely complex. Specifically, in LDH, the herniated nucleus pulposus exerts mechanical pressure on nerve roots, triggering local inflammation and consequent NP. Anoikis, a special form of programmed cell death, is closely related to the progression of NP. In this study, we sought to clarify the molecular characteristics of anoikis-related genes in NP, providing novel insights for the diagnosis and treatment of NP.

Methods: We screened NP-related genes based on the GSE124272 dataset and obtained 439 anoikis-related genes from the GeneCards database. Through Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) machine learning algorithms, six key hub genes were identified: hepatocyte growth factor (HGF), matrix metalloproteinase 13 (MMP13), c-abl oncogene 1, non-receptor tyrosine kinase (ABL1), elastase neutrophil expressed (ELANE), fatty acid synthase (FASN), and long non-coding RNA (Linc00324). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), alongside Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis, were performed on these hub genes. Additionally, transcription factors and potential therapeutic drugs were predicted. We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.

Results: Our data indicated that anoikis-related genes have diagnostic value in NP patients, as confirmed by experimental results. Moreover, this study elucidated the role of these genes in immune infiltration during the pathogenesis of NP and identified potential therapeutic drugs targeting these key genes.

Conclusion: This study further explores the pathogenesis of NP and provides certain reference value for developing targeted therapeutic strategies, thereby improving NP management.

利用机器学习预测和验证神经性疼痛中嗜酒相关基因。
背景:神经性疼痛(NP)可由多种临床情况引起,如脊髓损伤、腰椎间盘突出(LDH)、腰椎管狭窄、糖尿病、带状疱疹、脊髓肿瘤和炎症刺激。NP的发病机制极其复杂。具体来说,在LDH中,突出的髓核对神经根施加机械压力,引发局部炎症和随之而来的NP。Anoikis是一种特殊形式的程序性细胞死亡,与NP的进展密切相关。在本研究中,我们试图阐明NP中嗜酒相关基因的分子特征,为NP的诊断和治疗提供新的见解。方法:基于GSE124272数据集筛选np相关基因,并从GeneCards数据库中获得439个anoiki相关基因。通过最小绝对收缩和选择算子(LASSO)和支持向量机(SVM)机器学习算法,鉴定出6个关键枢纽基因:肝细胞生长因子(HGF)、基质金属蛋白酶13 (MMP13)、c-abl癌基因1、非受体酪氨酸激酶(ABL1)、弹性酶中性粒细胞表达(ELANE)、脂肪酸合成酶(FASN)和长链非编码RNA (Linc00324)。对这些枢纽基因进行功能富集分析,包括基因本体(GO)和京都基因与基因组百科全书(KEGG),以及基因集富集分析(GSEA)和免疫浸润分析。此外,对转录因子和潜在的治疗药物进行了预测。我们还使用大鼠构建NP模型,并使用苏木精和伊红(H&E)染色、实时聚合酶链反应(PCR)和Western blotting检测验证分析的枢纽基因。结果:我们的数据表明嗜酒相关基因在NP患者中具有诊断价值,实验结果证实了这一点。此外,本研究阐明了这些基因在NP发病过程中的免疫浸润作用,并确定了针对这些关键基因的潜在治疗药物。结论:本研究进一步探讨了NP的发病机制,为制定有针对性的治疗策略,从而提高NP的管理水平提供了一定的参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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